From ride-hailing apps and volatile gas prices to electric cars and (theoretically) autonomous vehicles, transportation behavior is rapidly changing. To properly plan for and manage our evolving transportation system, engineers and planners must keep pace with these changes. If managing transportation demand is important to your community, it’s not enough to follow the old pattern of creating new core analytics every 5-10 years to feed your models for any type of planning. For transportation demand management (TDM), which could be most profoundly affected by these new trends, the need for up-to-date, real, accurate data is even sharper.
In today’s evolving environment, effective TDM requires regular access to clean, up-to-date data. One problem that many planners face is that surveys and other traditional data-gathering methods simply cannot deliver high quality data at a frequent update cadence and affordable price.
Keep reading this blog post to learn all about TDM, and to find out how Big Data can help you maximize the impact of TDM strategies.
Note: This is a guest blog post from Wendy Tao, the Head of Business Development and Strategy of the Intelligent Transportation Systems Group at Siemens Mobility. Wendy helps communities develop Smart Cities solutions related to advanced traffic management systems, adaptive signal control, connected vehicles and multi-modal applications.
From Intelligent Transportation Systems (ITS) to Massive Mobile Data, innovative technologies are tackling decades old challenges and creating new opportunities in the transportation industry. And it’s not just an idea. We’re seeing significant impacts derived from in-depth evaluations on project performance and cost-effectiveness. Siemens recently partnered with StreetLight Data to measure the impact of a Siemens’ SCOOT adaptive signal control implementation in Ann Arbor, MI. Our empirical before-and-after study showed that SCOOT can reduce travel times by 10 to 20 percent. The study used archival navigation-GPS data from connected cars.
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The smart city movement’s first wave brought tons of stationary sensors to our cities, especially in the context of transportation. These sensors are passively collecting valuable travel pattern information at traffic lights, parking lots, bus stops, sidewalks, and more. But if we want cities that are truly smart – if we want to solve the challenges exposed by our stationary sensors – we have to go beyond them. In this blog post, I will use New York City as a case study to explain why.
This analysis was done in conjunction with our friends at MotionLoft. Thanks to them and to the team at Great Wall of Oakland. Also thanks to Ozumo restaurant and The Broadway Grand apartments for donating the location and powersource for the Motionloft sensors.
Northern Virginia (NOVA) has tremendous traffic congestion, but limited room to expand highways. The state and local governments want to expand and enhance the suite of Transportation Demand Management programs and investments to reduce travel demand on the highways through approaches like carpooling, transit, bike, and more.